Robotic thyroidectomy offers improved cosmetic outcomes compared to traditional open surgery. However, the risk of recurrent laryngeal nerve (RLN) injury remains a concern, especially during the learning curve. Intraoperative nerve monitoring (IONM) has been proposed as a tool to enhance RLN identification and preservation. This chapter examines the current literature on the use of IONM in robotic thyroidectomy. While some studies have demonstrated a reduction in RLN injury rates with IONM, especially in high-risk patients, the overall evidence is inconsistent. Factors such as surgical experience, type of robotic approach and IONM technique, and patient characteristics may influence the effectiveness of IONM. Furthermore, challenges in applying IONM to robotic procedures, such as altered surgical views and narrow working spaces, have been discussed. Despite these limitations, IONM shows promise in improving the safety of robotic thyroidectomy. Indeed, bilateral RLN injury can be prevented, and the RLN can be identified and mapped during dissection, which is especially beneficial for surgeons new to robotic thyroidectomy due to the unfamiliar visualization and confined workspace. Additionally, continuous intraoperative nerve monitoring (C-IONM) provides real-time alerts for impending traction or thermal injury, a crucial feature in energy device-based surgery lacking force feedback.